Optimize a function

# import libraries
import jax.numpy as jnp
from optymus import Optimizer
from optymus.benchmark import MccormickFunction

# define the objective function
f = MccormickFunction()

# define initial guess
initial_point = jnp.array([-3.0, 2.0])

# instantiate the optimizer with the objective function and the initial point
# select the method to be used
opt = Optimizer(
    f_obj=f,
    x0=initial_point,
    method='newton_raphson',
    h_type='hessian',
)

Newton-Raphson 0:   3%|▎         | 3/100 [00:01<00:53,  1.81it/s]

# print a short report about the optimization process
opt.print_report()

	Method	Initial Guess	Optimal Solution	Objective Function Value	Number of Iterations	Time Elapsed
Optimization Results	newton_raphson	[-3.0, 2.0]	[-0.5471975467892413, -1.5471975535583]	-1.9132229549810367	3	1.658297

# plot the results
opt.plot_results(min=-5, max=5, show=True, notebook=True)